Overview

Dataset statistics

Number of variables28
Number of observations2240
Missing cells24
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory490.1 KiB
Average record size in memory224.1 B

Variable types

Numeric15
Categorical12
DateTime1

Alerts

Income is highly overall correlated with MntFishProducts and 9 other fieldsHigh correlation
Kidhome is highly overall correlated with MntMeatProducts and 3 other fieldsHigh correlation
MntFishProducts is highly overall correlated with Income and 7 other fieldsHigh correlation
MntFruits is highly overall correlated with Income and 7 other fieldsHigh correlation
MntGoldProds is highly overall correlated with Income and 8 other fieldsHigh correlation
MntMeatProducts is highly overall correlated with Income and 9 other fieldsHigh correlation
MntSweetProducts is highly overall correlated with Income and 7 other fieldsHigh correlation
MntWines is highly overall correlated with Income and 9 other fieldsHigh correlation
NumCatalogPurchases is highly overall correlated with Income and 10 other fieldsHigh correlation
NumStorePurchases is highly overall correlated with Income and 9 other fieldsHigh correlation
NumWebPurchases is highly overall correlated with Income and 5 other fieldsHigh correlation
NumWebVisitsMonth is highly overall correlated with Income and 1 other fieldsHigh correlation
AcceptedCmp3 is highly imbalanced (62.4%)Imbalance
AcceptedCmp4 is highly imbalanced (61.7%)Imbalance
AcceptedCmp5 is highly imbalanced (62.4%)Imbalance
AcceptedCmp1 is highly imbalanced (65.6%)Imbalance
AcceptedCmp2 is highly imbalanced (89.7%)Imbalance
Complain is highly imbalanced (92.3%)Imbalance
Income has 24 (1.1%) missing valuesMissing
ID has unique valuesUnique
Recency has 28 (1.2%) zerosZeros
MntFruits has 400 (17.9%) zerosZeros
MntFishProducts has 384 (17.1%) zerosZeros
MntSweetProducts has 419 (18.7%) zerosZeros
MntGoldProds has 61 (2.7%) zerosZeros
NumDealsPurchases has 46 (2.1%) zerosZeros
NumWebPurchases has 49 (2.2%) zerosZeros
NumCatalogPurchases has 586 (26.2%) zerosZeros

Reproduction

Analysis started2024-11-19 01:05:47.113678
Analysis finished2024-11-19 01:07:12.956349
Duration1 minute and 25.84 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIQUE 

Distinct2240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5592.1598
Minimum0
Maximum11191
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-11-19T08:07:13.660238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile576.85
Q12828.25
median5458.5
Q38427.75
95-th percentile10675.05
Maximum11191
Range11191
Interquartile range (IQR)5599.5

Descriptive statistics

Standard deviation3246.6622
Coefficient of variation (CV)0.58057393
Kurtosis-1.190028
Mean5592.1598
Median Absolute Deviation (MAD)2791
Skewness0.039831873
Sum12526438
Variance10540815
MonotonicityNot monotonic
2024-11-19T08:07:13.995192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1826 1
 
< 0.1%
5680 1
 
< 0.1%
4640 1
 
< 0.1%
2525 1
 
< 0.1%
9503 1
 
< 0.1%
10704 1
 
< 0.1%
2669 1
 
< 0.1%
10037 1
 
< 0.1%
3726 1
 
< 0.1%
10872 1
 
< 0.1%
Other values (2230) 2230
99.6%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
9 1
< 0.1%
13 1
< 0.1%
17 1
< 0.1%
20 1
< 0.1%
22 1
< 0.1%
24 1
< 0.1%
25 1
< 0.1%
35 1
< 0.1%
ValueCountFrequency (%)
11191 1
< 0.1%
11188 1
< 0.1%
11187 1
< 0.1%
11181 1
< 0.1%
11178 1
< 0.1%
11176 1
< 0.1%
11171 1
< 0.1%
11166 1
< 0.1%
11148 1
< 0.1%
11133 1
< 0.1%

Year_Birth
Real number (ℝ)

Distinct59
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.8058
Minimum1893
Maximum1996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-11-19T08:07:14.345605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1893
5-th percentile1950
Q11959
median1970
Q31977
95-th percentile1988
Maximum1996
Range103
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.984069
Coefficient of variation (CV)0.0060869739
Kurtosis0.71746444
Mean1968.8058
Median Absolute Deviation (MAD)9
Skewness-0.34994386
Sum4410125
Variance143.61792
MonotonicityNot monotonic
2024-11-19T08:07:14.692602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1976 89
 
4.0%
1971 87
 
3.9%
1975 83
 
3.7%
1972 79
 
3.5%
1970 77
 
3.4%
1978 77
 
3.4%
1965 74
 
3.3%
1973 74
 
3.3%
1969 71
 
3.2%
1974 69
 
3.1%
Other values (49) 1460
65.2%
ValueCountFrequency (%)
1893 1
 
< 0.1%
1899 1
 
< 0.1%
1900 1
 
< 0.1%
1940 1
 
< 0.1%
1941 1
 
< 0.1%
1943 7
0.3%
1944 7
0.3%
1945 8
0.4%
1946 16
0.7%
1947 16
0.7%
ValueCountFrequency (%)
1996 2
 
0.1%
1995 5
 
0.2%
1994 3
 
0.1%
1993 5
 
0.2%
1992 13
0.6%
1991 15
0.7%
1990 18
0.8%
1989 30
1.3%
1988 29
1.3%
1987 27
1.2%

Education
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Graduation
1127 
PhD
486 
Master
370 
2n Cycle
203 
Basic
 
54

Length

Max length10
Median length10
Mean length7.51875
Min length3

Characters and Unicode

Total characters16842
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowGraduation
3rd rowGraduation
4th rowGraduation
5th rowGraduation

Common Values

ValueCountFrequency (%)
Graduation 1127
50.3%
PhD 486
21.7%
Master 370
 
16.5%
2n Cycle 203
 
9.1%
Basic 54
 
2.4%

Length

2024-11-19T08:07:15.024692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T08:07:15.328912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
graduation 1127
46.1%
phd 486
19.9%
master 370
 
15.1%
2n 203
 
8.3%
cycle 203
 
8.3%
basic 54
 
2.2%

Most occurring characters

ValueCountFrequency (%)
a 2678
15.9%
r 1497
8.9%
t 1497
8.9%
n 1330
 
7.9%
i 1181
 
7.0%
G 1127
 
6.7%
d 1127
 
6.7%
u 1127
 
6.7%
o 1127
 
6.7%
e 573
 
3.4%
Other values (12) 3578
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16842
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2678
15.9%
r 1497
8.9%
t 1497
8.9%
n 1330
 
7.9%
i 1181
 
7.0%
G 1127
 
6.7%
d 1127
 
6.7%
u 1127
 
6.7%
o 1127
 
6.7%
e 573
 
3.4%
Other values (12) 3578
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16842
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2678
15.9%
r 1497
8.9%
t 1497
8.9%
n 1330
 
7.9%
i 1181
 
7.0%
G 1127
 
6.7%
d 1127
 
6.7%
u 1127
 
6.7%
o 1127
 
6.7%
e 573
 
3.4%
Other values (12) 3578
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16842
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2678
15.9%
r 1497
8.9%
t 1497
8.9%
n 1330
 
7.9%
i 1181
 
7.0%
G 1127
 
6.7%
d 1127
 
6.7%
u 1127
 
6.7%
o 1127
 
6.7%
e 573
 
3.4%
Other values (12) 3578
21.2%

Marital_Status
Categorical

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Married
864 
Together
580 
Single
480 
Divorced
232 
Widow
 
77
Other values (3)
 
7

Length

Max length8
Median length7
Mean length7.0732143
Min length4

Characters and Unicode

Total characters15844
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDivorced
2nd rowSingle
3rd rowMarried
4th rowTogether
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 864
38.6%
Together 580
25.9%
Single 480
21.4%
Divorced 232
 
10.4%
Widow 77
 
3.4%
Alone 3
 
0.1%
YOLO 2
 
0.1%
Absurd 2
 
0.1%

Length

2024-11-19T08:07:15.661403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T08:07:15.980647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
married 864
38.6%
together 580
25.9%
single 480
21.4%
divorced 232
 
10.4%
widow 77
 
3.4%
alone 3
 
0.1%
yolo 2
 
0.1%
absurd 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 2739
17.3%
r 2542
16.0%
i 1653
10.4%
d 1175
7.4%
g 1060
 
6.7%
o 892
 
5.6%
M 864
 
5.5%
a 864
 
5.5%
T 580
 
3.7%
t 580
 
3.7%
Other values (16) 2895
18.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15844
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2739
17.3%
r 2542
16.0%
i 1653
10.4%
d 1175
7.4%
g 1060
 
6.7%
o 892
 
5.6%
M 864
 
5.5%
a 864
 
5.5%
T 580
 
3.7%
t 580
 
3.7%
Other values (16) 2895
18.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15844
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2739
17.3%
r 2542
16.0%
i 1653
10.4%
d 1175
7.4%
g 1060
 
6.7%
o 892
 
5.6%
M 864
 
5.5%
a 864
 
5.5%
T 580
 
3.7%
t 580
 
3.7%
Other values (16) 2895
18.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15844
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2739
17.3%
r 2542
16.0%
i 1653
10.4%
d 1175
7.4%
g 1060
 
6.7%
o 892
 
5.6%
M 864
 
5.5%
a 864
 
5.5%
T 580
 
3.7%
t 580
 
3.7%
Other values (16) 2895
18.3%

Income
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1974
Distinct (%)89.1%
Missing24
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean52247.251
Minimum1730
Maximum666666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-11-19T08:07:16.334517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile18985.5
Q135303
median51381.5
Q368522
95-th percentile84130
Maximum666666
Range664936
Interquartile range (IQR)33219

Descriptive statistics

Standard deviation25173.077
Coefficient of variation (CV)0.48180672
Kurtosis159.6367
Mean52247.251
Median Absolute Deviation (MAD)16557.5
Skewness6.7634874
Sum1.1577991 × 108
Variance6.3368379 × 108
MonotonicityNot monotonic
2024-11-19T08:07:16.669476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7500 12
 
0.5%
35860 4
 
0.2%
80134 3
 
0.1%
63841 3
 
0.1%
47025 3
 
0.1%
34176 3
 
0.1%
37760 3
 
0.1%
48432 3
 
0.1%
83844 3
 
0.1%
46098 3
 
0.1%
Other values (1964) 2176
97.1%
(Missing) 24
 
1.1%
ValueCountFrequency (%)
1730 1
< 0.1%
2447 1
< 0.1%
3502 1
< 0.1%
4023 1
< 0.1%
4428 1
< 0.1%
4861 1
< 0.1%
5305 1
< 0.1%
5648 1
< 0.1%
6560 1
< 0.1%
6835 1
< 0.1%
ValueCountFrequency (%)
666666 1
< 0.1%
162397 1
< 0.1%
160803 1
< 0.1%
157733 1
< 0.1%
157243 1
< 0.1%
157146 1
< 0.1%
156924 1
< 0.1%
153924 1
< 0.1%
113734 1
< 0.1%
105471 1
< 0.1%

Kidhome
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
1293 
1
899 
2
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Length

2024-11-19T08:07:17.025481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T08:07:17.288841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Teenhome
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
1158 
1
1030 
2
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%

Length

2024-11-19T08:07:17.553232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T08:07:17.795251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%
Distinct663
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Minimum2012-07-30 00:00:00
Maximum2014-06-29 00:00:00
2024-11-19T08:07:18.087374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:18.420620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Recency
Real number (ℝ)

ZEROS 

Distinct100
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.109375
Minimum0
Maximum99
Zeros28
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-11-19T08:07:18.777551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median49
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.962453
Coefficient of variation (CV)0.58975405
Kurtosis-1.2018968
Mean49.109375
Median Absolute Deviation (MAD)25
Skewness-0.0019866586
Sum110005
Variance838.82367
MonotonicityIncreasing
2024-11-19T08:07:19.124384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 37
 
1.7%
54 32
 
1.4%
30 32
 
1.4%
46 31
 
1.4%
49 30
 
1.3%
65 30
 
1.3%
92 30
 
1.3%
3 29
 
1.3%
71 29
 
1.3%
29 29
 
1.3%
Other values (90) 1931
86.2%
ValueCountFrequency (%)
0 28
1.2%
1 24
1.1%
2 28
1.2%
3 29
1.3%
4 27
1.2%
5 15
0.7%
6 21
0.9%
7 12
0.5%
8 25
1.1%
9 24
1.1%
ValueCountFrequency (%)
99 17
0.8%
98 22
1.0%
97 20
0.9%
96 25
1.1%
95 19
0.8%
94 26
1.2%
93 21
0.9%
92 30
1.3%
91 18
0.8%
90 20
0.9%

MntWines
Real number (ℝ)

HIGH CORRELATION 

Distinct776
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean303.93571
Minimum0
Maximum1493
Zeros13
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-11-19T08:07:19.463248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123.75
median173.5
Q3504.25
95-th percentile1000
Maximum1493
Range1493
Interquartile range (IQR)480.5

Descriptive statistics

Standard deviation336.59739
Coefficient of variation (CV)1.1074625
Kurtosis0.59874359
Mean303.93571
Median Absolute Deviation (MAD)164.5
Skewness1.1757706
Sum680816
Variance113297.8
MonotonicityNot monotonic
2024-11-19T08:07:19.818133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 42
 
1.9%
5 40
 
1.8%
6 37
 
1.7%
1 37
 
1.7%
4 33
 
1.5%
3 30
 
1.3%
8 30
 
1.3%
9 28
 
1.2%
12 25
 
1.1%
14 24
 
1.1%
Other values (766) 1914
85.4%
ValueCountFrequency (%)
0 13
 
0.6%
1 37
1.7%
2 42
1.9%
3 30
1.3%
4 33
1.5%
5 40
1.8%
6 37
1.7%
7 22
1.0%
8 30
1.3%
9 28
1.2%
ValueCountFrequency (%)
1493 1
< 0.1%
1492 2
0.1%
1486 1
< 0.1%
1478 2
0.1%
1462 1
< 0.1%
1459 1
< 0.1%
1449 1
< 0.1%
1396 1
< 0.1%
1394 1
< 0.1%
1379 1
< 0.1%

MntFruits
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct158
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.302232
Minimum0
Maximum199
Zeros400
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-11-19T08:07:20.168385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile123
Maximum199
Range199
Interquartile range (IQR)32

Descriptive statistics

Standard deviation39.773434
Coefficient of variation (CV)1.5121695
Kurtosis4.0509763
Mean26.302232
Median Absolute Deviation (MAD)8
Skewness2.1020633
Sum58917
Variance1581.926
MonotonicityNot monotonic
2024-11-19T08:07:20.523953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 400
 
17.9%
1 162
 
7.2%
2 120
 
5.4%
3 116
 
5.2%
4 104
 
4.6%
7 67
 
3.0%
5 65
 
2.9%
6 62
 
2.8%
12 50
 
2.2%
8 48
 
2.1%
Other values (148) 1046
46.7%
ValueCountFrequency (%)
0 400
17.9%
1 162
7.2%
2 120
 
5.4%
3 116
 
5.2%
4 104
 
4.6%
5 65
 
2.9%
6 62
 
2.8%
7 67
 
3.0%
8 48
 
2.1%
9 35
 
1.6%
ValueCountFrequency (%)
199 2
0.1%
197 1
 
< 0.1%
194 3
0.1%
193 2
0.1%
190 1
 
< 0.1%
189 1
 
< 0.1%
185 2
0.1%
184 1
 
< 0.1%
183 3
0.1%
181 1
 
< 0.1%

MntMeatProducts
Real number (ℝ)

HIGH CORRELATION 

Distinct558
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.95
Minimum0
Maximum1725
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-11-19T08:07:20.861068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116
median67
Q3232
95-th percentile687.1
Maximum1725
Range1725
Interquartile range (IQR)216

Descriptive statistics

Standard deviation225.71537
Coefficient of variation (CV)1.3519938
Kurtosis5.5167241
Mean166.95
Median Absolute Deviation (MAD)59
Skewness2.0832331
Sum373968
Variance50947.429
MonotonicityNot monotonic
2024-11-19T08:07:21.202358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 53
 
2.4%
5 50
 
2.2%
11 49
 
2.2%
8 46
 
2.1%
6 43
 
1.9%
10 40
 
1.8%
3 40
 
1.8%
9 38
 
1.7%
16 36
 
1.6%
12 35
 
1.6%
Other values (548) 1810
80.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 14
 
0.6%
2 30
1.3%
3 40
1.8%
4 30
1.3%
5 50
2.2%
6 43
1.9%
7 53
2.4%
8 46
2.1%
9 38
1.7%
ValueCountFrequency (%)
1725 2
0.1%
1622 1
< 0.1%
1607 1
< 0.1%
1582 1
< 0.1%
984 1
< 0.1%
981 1
< 0.1%
974 1
< 0.1%
968 1
< 0.1%
961 1
< 0.1%
951 2
0.1%

MntFishProducts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct182
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.525446
Minimum0
Maximum259
Zeros384
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-11-19T08:07:21.769735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q350
95-th percentile168.05
Maximum259
Range259
Interquartile range (IQR)47

Descriptive statistics

Standard deviation54.628979
Coefficient of variation (CV)1.4557849
Kurtosis3.0964609
Mean37.525446
Median Absolute Deviation (MAD)12
Skewness1.919769
Sum84057
Variance2984.3254
MonotonicityNot monotonic
2024-11-19T08:07:22.112192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 384
 
17.1%
2 156
 
7.0%
3 130
 
5.8%
4 108
 
4.8%
6 82
 
3.7%
7 66
 
2.9%
8 58
 
2.6%
10 55
 
2.5%
13 48
 
2.1%
12 47
 
2.1%
Other values (172) 1106
49.4%
ValueCountFrequency (%)
0 384
17.1%
1 10
 
0.4%
2 156
7.0%
3 130
 
5.8%
4 108
 
4.8%
5 1
 
< 0.1%
6 82
 
3.7%
7 66
 
2.9%
8 58
 
2.6%
10 55
 
2.5%
ValueCountFrequency (%)
259 1
 
< 0.1%
258 3
0.1%
254 1
 
< 0.1%
253 1
 
< 0.1%
250 3
0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
242 1
 
< 0.1%
240 2
0.1%
237 2
0.1%

MntSweetProducts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct177
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.062946
Minimum0
Maximum263
Zeros419
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-11-19T08:07:22.439387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile126
Maximum263
Range263
Interquartile range (IQR)32

Descriptive statistics

Standard deviation41.280498
Coefficient of variation (CV)1.5253512
Kurtosis4.3765483
Mean27.062946
Median Absolute Deviation (MAD)8
Skewness2.1360807
Sum60621
Variance1704.0796
MonotonicityNot monotonic
2024-11-19T08:07:22.779846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 419
 
18.7%
1 161
 
7.2%
2 128
 
5.7%
3 101
 
4.5%
4 82
 
3.7%
5 65
 
2.9%
6 64
 
2.9%
7 57
 
2.5%
8 56
 
2.5%
12 45
 
2.0%
Other values (167) 1062
47.4%
ValueCountFrequency (%)
0 419
18.7%
1 161
 
7.2%
2 128
 
5.7%
3 101
 
4.5%
4 82
 
3.7%
5 65
 
2.9%
6 64
 
2.9%
7 57
 
2.5%
8 56
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
263 1
 
< 0.1%
262 1
 
< 0.1%
198 1
 
< 0.1%
197 1
 
< 0.1%
196 1
 
< 0.1%
195 1
 
< 0.1%
194 3
0.1%
192 3
0.1%
191 1
 
< 0.1%
189 2
0.1%

MntGoldProds
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct213
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.021875
Minimum0
Maximum362
Zeros61
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-11-19T08:07:23.109548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median24
Q356
95-th percentile165.05
Maximum362
Range362
Interquartile range (IQR)47

Descriptive statistics

Standard deviation52.167439
Coefficient of variation (CV)1.1850345
Kurtosis3.5517093
Mean44.021875
Median Absolute Deviation (MAD)18
Skewness1.8861056
Sum98609
Variance2721.4417
MonotonicityNot monotonic
2024-11-19T08:07:23.447747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 73
 
3.3%
4 70
 
3.1%
3 69
 
3.1%
5 63
 
2.8%
12 63
 
2.8%
2 62
 
2.8%
0 61
 
2.7%
6 57
 
2.5%
7 54
 
2.4%
10 49
 
2.2%
Other values (203) 1619
72.3%
ValueCountFrequency (%)
0 61
2.7%
1 73
3.3%
2 62
2.8%
3 69
3.1%
4 70
3.1%
5 63
2.8%
6 57
2.5%
7 54
2.4%
8 40
1.8%
9 44
2.0%
ValueCountFrequency (%)
362 1
< 0.1%
321 1
< 0.1%
291 1
< 0.1%
262 1
< 0.1%
249 1
< 0.1%
248 1
< 0.1%
247 1
< 0.1%
246 1
< 0.1%
245 1
< 0.1%
242 2
0.1%

NumDealsPurchases
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.325
Minimum0
Maximum15
Zeros46
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-11-19T08:07:23.729323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9322375
Coefficient of variation (CV)0.83106989
Kurtosis8.9369143
Mean2.325
Median Absolute Deviation (MAD)1
Skewness2.4185694
Sum5208
Variance3.7335418
MonotonicityNot monotonic
2024-11-19T08:07:23.995921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 970
43.3%
2 497
22.2%
3 297
 
13.3%
4 189
 
8.4%
5 94
 
4.2%
6 61
 
2.7%
0 46
 
2.1%
7 40
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
Other values (5) 24
 
1.1%
ValueCountFrequency (%)
0 46
 
2.1%
1 970
43.3%
2 497
22.2%
3 297
 
13.3%
4 189
 
8.4%
5 94
 
4.2%
6 61
 
2.7%
7 40
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
ValueCountFrequency (%)
15 7
 
0.3%
13 3
 
0.1%
12 4
 
0.2%
11 5
 
0.2%
10 5
 
0.2%
9 8
 
0.4%
8 14
 
0.6%
7 40
1.8%
6 61
2.7%
5 94
4.2%

NumWebPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0848214
Minimum0
Maximum27
Zeros49
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-11-19T08:07:24.264455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7787141
Coefficient of variation (CV)0.68025352
Kurtosis5.7031284
Mean4.0848214
Median Absolute Deviation (MAD)2
Skewness1.3827943
Sum9150
Variance7.7212523
MonotonicityNot monotonic
2024-11-19T08:07:24.533820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 373
16.7%
1 354
15.8%
3 336
15.0%
4 280
12.5%
5 220
9.8%
6 205
9.2%
7 155
6.9%
8 102
 
4.6%
9 75
 
3.3%
0 49
 
2.2%
Other values (5) 91
 
4.1%
ValueCountFrequency (%)
0 49
 
2.2%
1 354
15.8%
2 373
16.7%
3 336
15.0%
4 280
12.5%
5 220
9.8%
6 205
9.2%
7 155
6.9%
8 102
 
4.6%
9 75
 
3.3%
ValueCountFrequency (%)
27 2
 
0.1%
25 1
 
< 0.1%
23 1
 
< 0.1%
11 44
 
2.0%
10 43
 
1.9%
9 75
 
3.3%
8 102
4.6%
7 155
6.9%
6 205
9.2%
5 220
9.8%

NumCatalogPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6620536
Minimum0
Maximum28
Zeros586
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-11-19T08:07:24.770642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9231007
Coefficient of variation (CV)1.0980623
Kurtosis8.0474368
Mean2.6620536
Median Absolute Deviation (MAD)2
Skewness1.8809888
Sum5963
Variance8.5445174
MonotonicityNot monotonic
2024-11-19T08:07:25.010444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 586
26.2%
1 497
22.2%
2 276
12.3%
3 184
 
8.2%
4 182
 
8.1%
5 140
 
6.2%
6 128
 
5.7%
7 79
 
3.5%
8 55
 
2.5%
10 48
 
2.1%
Other values (4) 65
 
2.9%
ValueCountFrequency (%)
0 586
26.2%
1 497
22.2%
2 276
12.3%
3 184
 
8.2%
4 182
 
8.1%
5 140
 
6.2%
6 128
 
5.7%
7 79
 
3.5%
8 55
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
28 3
 
0.1%
22 1
 
< 0.1%
11 19
 
0.8%
10 48
 
2.1%
9 42
 
1.9%
8 55
 
2.5%
7 79
3.5%
6 128
5.7%
5 140
6.2%
4 182
8.1%

NumStorePurchases
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7901786
Minimum0
Maximum13
Zeros15
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-11-19T08:07:25.262373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q38
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2509581
Coefficient of variation (CV)0.56146077
Kurtosis-0.62204828
Mean5.7901786
Median Absolute Deviation (MAD)2
Skewness0.70223729
Sum12970
Variance10.568729
MonotonicityNot monotonic
2024-11-19T08:07:25.535104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 490
21.9%
4 323
14.4%
2 223
10.0%
5 212
9.5%
6 178
 
7.9%
8 149
 
6.7%
7 143
 
6.4%
10 125
 
5.6%
9 106
 
4.7%
12 105
 
4.7%
Other values (4) 186
 
8.3%
ValueCountFrequency (%)
0 15
 
0.7%
1 7
 
0.3%
2 223
10.0%
3 490
21.9%
4 323
14.4%
5 212
9.5%
6 178
 
7.9%
7 143
 
6.4%
8 149
 
6.7%
9 106
 
4.7%
ValueCountFrequency (%)
13 83
 
3.7%
12 105
 
4.7%
11 81
 
3.6%
10 125
 
5.6%
9 106
 
4.7%
8 149
6.7%
7 143
6.4%
6 178
7.9%
5 212
9.5%
4 323
14.4%

NumWebVisitsMonth
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3165179
Minimum0
Maximum20
Zeros11
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-11-19T08:07:25.791525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.426645
Coefficient of variation (CV)0.45643503
Kurtosis1.8216138
Mean5.3165179
Median Absolute Deviation (MAD)2
Skewness0.20792556
Sum11909
Variance5.888606
MonotonicityNot monotonic
2024-11-19T08:07:26.074976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7 393
17.5%
8 342
15.3%
6 340
15.2%
5 281
12.5%
4 218
9.7%
3 205
9.2%
2 202
9.0%
1 153
 
6.8%
9 83
 
3.7%
0 11
 
0.5%
Other values (6) 12
 
0.5%
ValueCountFrequency (%)
0 11
 
0.5%
1 153
 
6.8%
2 202
9.0%
3 205
9.2%
4 218
9.7%
5 281
12.5%
6 340
15.2%
7 393
17.5%
8 342
15.3%
9 83
 
3.7%
ValueCountFrequency (%)
20 3
 
0.1%
19 2
 
0.1%
17 1
 
< 0.1%
14 2
 
0.1%
13 1
 
< 0.1%
10 3
 
0.1%
9 83
 
3.7%
8 342
15.3%
7 393
17.5%
6 340
15.2%

AcceptedCmp3
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2077 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Length

2024-11-19T08:07:26.363379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T08:07:26.596505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

AcceptedCmp4
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2073 
1
 
167

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

Length

2024-11-19T08:07:26.831766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T08:07:27.106047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

AcceptedCmp5
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2077 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Length

2024-11-19T08:07:27.344833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T08:07:27.572348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

AcceptedCmp1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2096 
1
 
144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

Length

2024-11-19T08:07:27.821507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T08:07:28.050889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

AcceptedCmp2
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2210 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Length

2024-11-19T08:07:28.290106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T08:07:28.523406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Response
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
1906 
1
334 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Length

2024-11-19T08:07:28.757831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T08:07:28.987778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Complain
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2219 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Length

2024-11-19T08:07:29.230727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T08:07:29.464737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Country
Categorical

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Spain
1095 
Saudi Arabia
337 
Canada
268 
Australia
160 
India
148 
Other values (3)
232 

Length

Max length12
Median length5
Mean length6.4696429
Min length3

Characters and Unicode

Total characters14492
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpain
2nd rowCanada
3rd rowUSA
4th rowAustralia
5th rowSpain

Common Values

ValueCountFrequency (%)
Spain 1095
48.9%
Saudi Arabia 337
 
15.0%
Canada 268
 
12.0%
Australia 160
 
7.1%
India 148
 
6.6%
Germany 120
 
5.4%
USA 109
 
4.9%
Mexico 3
 
0.1%

Length

2024-11-19T08:07:29.717632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T08:07:29.998412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
spain 1095
42.5%
saudi 337
 
13.1%
arabia 337
 
13.1%
canada 268
 
10.4%
australia 160
 
6.2%
india 148
 
5.7%
germany 120
 
4.7%
usa 109
 
4.2%
mexico 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 3498
24.1%
i 2080
14.4%
n 1631
11.3%
S 1541
10.6%
p 1095
 
7.6%
d 753
 
5.2%
r 617
 
4.3%
A 606
 
4.2%
u 497
 
3.4%
337
 
2.3%
Other values (15) 1837
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14492
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3498
24.1%
i 2080
14.4%
n 1631
11.3%
S 1541
10.6%
p 1095
 
7.6%
d 753
 
5.2%
r 617
 
4.3%
A 606
 
4.2%
u 497
 
3.4%
337
 
2.3%
Other values (15) 1837
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14492
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3498
24.1%
i 2080
14.4%
n 1631
11.3%
S 1541
10.6%
p 1095
 
7.6%
d 753
 
5.2%
r 617
 
4.3%
A 606
 
4.2%
u 497
 
3.4%
337
 
2.3%
Other values (15) 1837
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14492
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3498
24.1%
i 2080
14.4%
n 1631
11.3%
S 1541
10.6%
p 1095
 
7.6%
d 753
 
5.2%
r 617
 
4.3%
A 606
 
4.2%
u 497
 
3.4%
337
 
2.3%
Other values (15) 1837
12.7%

Interactions

2024-11-19T08:07:07.774667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:50.687311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:54.249715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:58.032331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:03.802961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:08.490672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:12.918855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:19.753232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:26.060021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:31.176185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:37.644177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:42.775536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:47.376288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:55.057947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:03.437447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:08.006899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:50.919238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:54.468254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:58.252710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:04.033368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:08.892979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:13.150951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:20.087057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:26.387892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:31.487922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:37.916348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:43.101545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:47.670179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:56.216985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:03.840608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:08.251537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:51.140384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:54.681230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:58.476105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:04.263923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:09.374503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:13.373153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:20.759711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:26.724932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:31.743086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:38.176884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:43.386002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:47.952225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:57.293386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:04.126685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:08.492408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:51.370288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:54.911155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:58.708712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:04.506381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:09.648955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:13.991911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:21.469724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:27.050335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:32.642853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:38.452700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:43.682858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:48.221269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:57.987318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:04.411923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:08.725276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:51.581256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:55.224934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:58.922879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:04.730644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:09.954155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:14.347207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:22.066623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:27.340260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:33.119230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:38.710245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:43.954546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:48.474579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:58.362316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:04.685226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:08.976458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:51.810879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:55.466414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:59.162366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:04.966007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:10.283260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:14.639227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:22.557920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:27.672698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:33.606998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:39.005308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:44.245402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:48.738498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:58.787045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:04.977388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:09.244471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:52.048495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:55.854344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:59.572937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:05.200370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:10.540841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:14.913695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:23.182147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:28.092652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:34.540913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:39.285599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:44.543914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:49.051311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:59.627245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:05.290883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:09.490248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:52.287044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:56.106579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:00.015410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:05.435478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:10.788977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:15.222905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:23.441246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:28.490912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:35.208915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:39.559950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:44.843553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:49.473952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:00.044963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:05.604702image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:09.724914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:52.632525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:56.325285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:00.364411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:05.921957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:11.030912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:15.529054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:23.758417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:28.791080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:35.627652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:40.075260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:45.186719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:50.294848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:00.737794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:05.889406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:09.956158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:52.843640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:56.548905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:01.307521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:06.158255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:11.285534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:15.775758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:24.101220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:29.082206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:35.909867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:40.376137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:45.519261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:50.819651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:01.158231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:06.177310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:10.212981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:53.082891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:56.785480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:01.957479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:06.574855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:11.591738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:16.254945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:24.492533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:29.384008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:36.185531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:40.742768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:45.796026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:52.078116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:01.517820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:06.490059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:10.464243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:53.316711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:57.031363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:02.692286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:07.006777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:11.892969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:17.072793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:24.834970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:29.691841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:36.524855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:41.142616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:46.093947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:52.979025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:02.134713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:06.770264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:10.705164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:53.532380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:57.324373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:02.998241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:07.319637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:12.172043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:17.920607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:25.075654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:30.175207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:36.834605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:41.522523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:46.514022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:53.356543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:02.529603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:07.041792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:10.933074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:53.748163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:57.561180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:03.282328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:07.583940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:12.419493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:18.797227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:25.366685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:30.671840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:37.085533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:42.076122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:46.786879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:54.070584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:02.787141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:07.275887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:11.181040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:53.991837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:05:57.782842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:03.549978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:08.039193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:12.650345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:19.210676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:25.685691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:30.916148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:37.354106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:42.474395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:47.084175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:06:54.617120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:03.105124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-19T08:07:07.516049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-11-19T08:07:30.526487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
IncomeAcceptedCmp1AcceptedCmp2AcceptedCmp3AcceptedCmp4AcceptedCmp5ComplainCountryEducationIDKidhomeMarital_StatusMntFishProductsMntFruitsMntGoldProdsMntMeatProductsMntSweetProductsMntWinesNumCatalogPurchasesNumDealsPurchasesNumStorePurchasesNumWebPurchasesNumWebVisitsMonthRecencyResponseTeenhomeYear_Birth
Income1.0000.3620.0670.0000.1390.4490.0000.0000.0540.0040.2860.0000.5770.5820.5060.8170.5670.8300.792-0.1960.7320.573-0.6440.0080.1920.174-0.217
AcceptedCmp10.3621.0000.1660.0890.2470.3990.0000.0000.035-0.0220.1820.0310.2110.1780.1760.2850.2210.3040.301-0.1730.2010.183-0.194-0.0190.2910.145-0.006
AcceptedCmp20.0670.1661.0000.0610.2840.2130.0000.0000.017-0.0160.0790.000-0.0000.0030.0680.063-0.0010.1380.104-0.0520.0830.037-0.006-0.0020.1630.000-0.013
AcceptedCmp30.0000.0890.0611.0000.0730.0740.0000.0000.000-0.0360.0300.000-0.0240.0090.1390.009-0.0150.0350.104-0.014-0.0830.0330.060-0.0330.2510.0380.064
AcceptedCmp40.1390.2470.2840.0731.0000.3030.0000.0110.048-0.0250.1620.0000.0070.0300.0670.1430.0090.3110.187-0.0020.1990.177-0.0300.0190.1730.026-0.067
AcceptedCmp50.4490.3990.2130.0740.3031.0000.0000.0000.034-0.0070.2100.0270.2180.2370.1920.3310.2550.3660.324-0.2540.2260.175-0.2750.0010.3240.2050.015
Complain0.0000.0000.0000.0000.0000.0001.0000.0000.0390.0340.0270.000-0.029-0.012-0.035-0.025-0.026-0.038-0.0310.017-0.027-0.0220.0250.0130.0000.000-0.009
Country0.0000.0000.0000.0000.0110.0000.0001.0000.019-0.0040.0420.046-0.0080.003-0.0210.0130.0200.014-0.000-0.0120.017-0.0080.0160.0310.0510.0000.008
Education0.0540.0350.0170.0000.0480.0340.0390.0191.000-0.0060.0510.000-0.163-0.160-0.1340.066-0.1640.2320.0870.0260.0860.092-0.054-0.0170.0920.104-0.183
ID0.004-0.022-0.016-0.036-0.025-0.0070.034-0.004-0.0061.0000.0000.004-0.029-0.022-0.042-0.013-0.034-0.026-0.012-0.024-0.023-0.025-0.011-0.0460.0320.0000.003
Kidhome0.2860.1820.0790.0300.1620.2100.0270.0420.0510.0001.0000.040-0.453-0.450-0.426-0.551-0.439-0.580-0.5980.261-0.557-0.4220.4820.0060.0750.0540.259
Marital_Status0.0000.0310.0000.0000.0000.0270.0000.0460.0000.0040.0401.0000.0270.0040.0170.0250.0140.0130.022-0.034-0.003-0.006-0.0350.0170.1450.073-0.044
MntFishProducts0.5770.211-0.000-0.0240.0070.218-0.029-0.008-0.163-0.029-0.4530.0271.0000.7050.5650.7260.7010.5250.657-0.1200.5830.466-0.4580.0130.1310.138-0.031
MntFruits0.5820.1780.0030.0090.0300.237-0.0120.003-0.160-0.022-0.4500.0040.7051.0000.5690.7130.6910.5180.635-0.1100.5830.471-0.4430.0250.1520.121-0.025
MntGoldProds0.5060.1760.0680.1390.0670.192-0.035-0.021-0.134-0.042-0.4260.0170.5650.5691.0000.6380.5430.5750.6490.0900.5400.580-0.2610.0180.1380.056-0.077
MntMeatProducts0.8170.2850.0630.0090.1430.331-0.0250.0130.066-0.013-0.5510.0250.7260.7130.6381.0000.6960.8240.852-0.0320.7790.679-0.4920.0280.2430.227-0.112
MntSweetProducts0.5670.221-0.001-0.0150.0090.255-0.0260.020-0.164-0.034-0.4390.0140.7010.6910.5430.6961.0000.5050.628-0.1060.5810.464-0.4490.0240.1130.1010.003
MntWines0.8300.3040.1380.0350.3110.366-0.0380.0140.232-0.026-0.5800.0130.5250.5180.5750.8240.5051.0000.8230.0570.8070.740-0.3890.0190.2680.117-0.234
NumCatalogPurchases0.7920.3010.1040.1040.1870.324-0.031-0.0000.087-0.012-0.5980.0220.6570.6350.6490.8520.6280.8231.000-0.0400.7090.619-0.5360.0310.2190.119-0.179
NumDealsPurchases-0.196-0.173-0.052-0.014-0.002-0.2540.017-0.0120.026-0.0240.261-0.034-0.120-0.1100.090-0.032-0.1060.057-0.0401.0000.1000.2840.3980.0080.0960.347-0.087
NumStorePurchases0.7320.2010.083-0.0830.1990.226-0.0270.0170.086-0.023-0.557-0.0030.5830.5830.5400.7790.5810.8070.7090.1001.0000.673-0.4540.0060.1490.085-0.168
NumWebPurchases0.5730.1830.0370.0330.1770.175-0.022-0.0080.092-0.025-0.422-0.0060.4660.4710.5800.6790.4640.7400.6190.2840.6731.000-0.097-0.0040.1660.160-0.164
NumWebVisitsMonth-0.644-0.194-0.0060.060-0.030-0.2750.0250.016-0.054-0.0110.482-0.035-0.458-0.443-0.261-0.492-0.449-0.389-0.5360.398-0.454-0.0971.000-0.0220.1210.2170.131
Recency0.008-0.019-0.002-0.0330.0190.0010.0130.031-0.017-0.0460.0060.0170.0130.0250.0180.0280.0240.0190.0310.0080.006-0.004-0.0221.0000.2080.050-0.021
Response0.1920.2910.1630.2510.1730.3240.0000.0510.0920.0320.0750.1450.1310.1520.1380.2430.1130.2680.2190.0960.1490.1660.1210.2081.0000.1590.021
Teenhome0.1740.1450.0000.0380.0260.2050.0000.0000.1040.0000.0540.0730.1380.1210.0560.2270.1010.1170.1190.3470.0850.1600.2170.0500.1591.000-0.386
Year_Birth-0.217-0.006-0.0130.064-0.0670.015-0.0090.008-0.1830.0030.259-0.044-0.031-0.025-0.077-0.1120.003-0.234-0.179-0.087-0.168-0.1640.131-0.0210.021-0.3861.000

Missing values

2024-11-19T08:07:11.606520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-19T08:07:12.495116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ResponseComplainCountry
018261970GraduationDivorced84835.0002014-06-160189104379111189218144610000010Spain
111961GraduationSingle57091.0002014-06-1504645647037173750000110Canada
2104761958GraduationMarried67267.0012014-05-130134115915230132520000000USA
313861967GraduationTogether32474.0112014-05-1101001000110270000000Australia
453711989GraduationSingle21474.0102014-04-0806162411034231271000010Spain
573481958PhDSingle71691.0002014-03-1703361304112403243147520000010Spain
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